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Cost Optimizationcommercial2026-03-01•7 min read•Reviewed 2026-03-01

Groq vs Together AI Pricing: Budget LLM APIs Compared

Groq and Together AI both offer API access to open source LLMs at prices far below OpenAI and Anthropic. Groq differentiates with ultra-fast inference via custom LPU hardware, while Together AI offers the widest model selection at competitive prices. For teams looking to minimize LLM API costs without self-hosting, these two providers represent the best budget options in 2026.

Key Takeaways

  • Use project-level visibility to link AI usage with product outcomes.
  • Track spend, latency, errors, and request logs together to make stronger decisions.
  • Apply alerts and operational guardrails before traffic volume scales.

Proof from the product

Real UI snapshot used to anchor the operational workflow described in this article.

Groq vs Together AI Pricing: Budget LLM APIs Compared supporting screenshot

How does Groq pricing work?

Groq offers API access to popular open source models (Llama, Mistral, Mixtral) at competitive per-token pricing. The key differentiator is speed — Groq LPU hardware delivers inference speeds 10-18x faster than GPU-based alternatives. This speed advantage matters for real-time applications and can reduce costs for latency-sensitive workloads where faster inference means fewer concurrent connections needed.

How does Together AI pricing work?

Together AI provides API access to 100+ open source models with per-token pricing that varies by model size. Pricing is transparent and competitive, often 5-10x cheaper than equivalent proprietary APIs. Together AI also offers serverless and dedicated deployment options, letting teams choose between pay-per-token and reserved capacity based on usage patterns.

Cost comparison: Groq vs Together AI

For the same models (Llama 3 70B, Mixtral), pricing is similar between Groq and Together AI. The difference is in speed vs selection: Groq delivers faster inference but supports fewer models. Together AI supports more models and offers more deployment flexibility. For high-throughput workloads, compare total cost including latency impact — faster inference from Groq may reduce infrastructure costs elsewhere.

When to choose Groq vs Together AI

Choose Groq when: inference speed is critical (real-time chat, streaming), you need consistent low-latency responses, or your workload fits supported models. Choose Together AI when: you need access to many different models, you want dedicated GPU instances, or you need fine-tuning capabilities. Both are excellent alternatives to expensive proprietary APIs.

How to track costs across budget LLM providers

Connect both Groq and Together AI API keys to AI Cost Board. Compare actual per-request costs between providers and against proprietary alternatives. Set budget alerts to ensure cost savings materialize. Monitor usage patterns to identify optimization opportunities — sometimes splitting workloads across providers yields the best cost-performance ratio.